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Applied Sciences
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Intelligent Transportation System Technologies and Applications, 2nd Edition
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Intelligent Transportation System Technologies and Applications, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 25 August 2025 | Viewed by 318

Special Issue Editors

Dr. Mahjoub Dridi

E-Mail Website
Guest Editor
Laboratoire Connaissance et Intelligence Artificielle Distribuées (CIAD), University Bourgogne Franche-Comté, UTBM, 90010 Belfort, France
Interests: autonomous intersections; transportation systems; traffic control; urban mobility; combinatorial optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Yazan Mualla

E-Mail Website
Guest Editor
Laboratoire Connaissance et Intelligence Artificielle Distribuées (CIAD), University of Technology of Belfort-Montbéliard (UTBM), 90010 Belfort, France
Interests: explainable artificial intelligence (XAI); human computer interaction (HCI); multiagent systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Abdeljalil Abbas-Turki

E-Mail Website
Guest Editor
Laboratoire Connaissances et Intelligence Artificielle Distribuées, Université de Technologie de Belfort-Montbéliard, Belfort, France
Interests: connected autonomous vehicles; cooperative driving; artificial intelligence; control theory; urban mobility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Traffic congestion is among the largest sources of pollution and noise, not to mention an enormous waste of time and energy. Vehicle traffic rationalization and optimization have become mandatory to at least minimize the impact of pollutant emissions and unsustainable fuel consumption in cities and urban areas. Intelligent transportation systems (ITSs) constitute a fertile research area to manage urban traffic in smart cities and improve transportation efficiency, environmental care, and safety. As science harnesses the technological progress in the ITS domain, paradigm shifts are anticipated.

This Special Issue aims to study the various advanced technologies and applications of intelligent transport systems and highlight their contributions in terms of reducing traffic congestion in cities, improving the safety of vulnerable road users, reducing pollution, increasing the attractiveness of cities and thus supporting the economy of cities. Topics of interest include (but are not limited to) the following:

  • Traffic signal management;
  • Autonomous intersection management;
  • Explainable AI and intelligent transportation;
  • Navigation in smart cities;
  • Cloud services for smart mobility;
  • Control and management of electric and hybrid vehicles;
  • Multi-agent systems;
  • Combinatorial optimization;
  • Meta-heuristics;
  • Reinforcement learning;
  • Deep learning;
  • Petri nets modelling and control;
  • Connected vehicles;
  • Cooperative driving;
  • Computer vision and smart transportation systems.

Dr. Mahjoub Dridi
Dr. Yazan Mualla
Prof. Dr. Abdeljalil Abbas-Turki
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cooperative driving
  • traffic control
  • urban mobility
  • explainability
  • smart mobility

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Published Papers (1 paper)

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Research

28 pages, 2345 KiB  
Article
Towards Synthetic Augmentation of Training Datasets Generated by Mobility-on-Demand Service Using Deep Variational Autoencoders
by Martin Gregurić, Filip Vrbanić and Edouard Ivanjko
Appl. Sci. 2025, 15(9), 4708; https://doi.org/10.3390/app15094708 - 24 Apr 2025
Viewed by 116
Abstract
The machine learning-based approaches for analysing the mobility needs of users are currently the most prevalent approach in the mobility-on-demand (MoD) analysis. Their efficiency relies on the comprehensiveness and consistency of training datasets. However, this is also the biggest challenge, as high-quality training [...] Read more.
The machine learning-based approaches for analysing the mobility needs of users are currently the most prevalent approach in the mobility-on-demand (MoD) analysis. Their efficiency relies on the comprehensiveness and consistency of training datasets. However, this is also the biggest challenge, as high-quality training data are often difficult to obtain. Thus, the Variational Autoencoders (VAE) are investigated as potential generators of synthetic samples for the augmentation of MoD-based datasets. This MoD-based dataset is created using real-world taxi trip data recorded in the Manhattan district of New York City, USA. This augmentation by synthetic samples can potentially enable larger, balanced, and more consistent datasets for machine learning analysis of MoD-based data. The proposed VAE approaches are compared with common dimensionality reduction techniques and standard autoencoders concerning their efficiency in 2-dimensional clustering based on collected MoD-based data. The proposed 2-dimensional convolution VAE framework has achieved clustering results comparable with the other analysed approaches. Thus, it generates synthetic samples, known as “deepfakes”. They are added in different percentages to the initial dataset based on real-world MoD-based data. Thus, this creates augmented datasets of the initial one. The models for predicting the cluster of each sample are used to evaluate the impact of those augmented datasets on their accuracy and learning convergence compared to the initial dataset. Results have shown that the accuracy and learning convergence are improved if those predictive models are trained on an augmented dataset which includes up to 10% of synthetic samples for each cluster. Full article
(This article belongs to the Special Issue Intelligent Transportation System Technologies and Applications, 2nd Edition)
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